1932

Abstract

Metal ions play a critical role in various chemical, biological, and environmental processes. This review reports on emerging chemical mechanisms in the catalysis of DNA and RNA. We provide an overview of the metal-dependent mechanisms of DNA cleavage in CRISPR (clustered regularly interspaced short palindromic repeats)-Cas systems that are transforming life sciences through genome editing technologies, and showcase intriguing metal-dependent mechanisms of RNA cleavages. We show that newly discovered CRISPR-Cas complexes operate as protein-assisted ribozymes, highlighting RNA's versatility and the enhancement of CRISPR-Cas functions through strategic metal ion use. We demonstrate the power of computer simulations in observing chemical processes as they unfold and in advancing structural biology through innovative approaches for refining cryo-electron microscopy maps. Understanding metal ion involvement in nucleic acid catalysis is crucial for advancing genome editing, aiding therapeutic interventions for genetic disorders, and improving the editing tools’ specificity and efficiency.

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2025-04-21
2025-06-25
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Literature Cited

  1. 1.
    Waldron KJ, Rutherford JC, Ford D, Robinson NJ. 2009.. Metalloproteins and metal sensing. . Nature 460:(7257):82330
    [Crossref] [Google Scholar]
  2. 2.
    Ward WL, Plakos K, DeRose VJ. 2014.. Nucleic acid catalysis: metals, nucleobases, and other cofactors. . Chem. Rev. 114:(8):431842
    [Crossref] [Google Scholar]
  3. 3.
    Dupureur CM. 2008.. Roles of metal ions in nucleases. . Curr. Opin. Chem. Biol. 12:(2):25055
    [Crossref] [Google Scholar]
  4. 4.
    Steitz TA. 1993.. DNA- and RNA-dependent DNA polymerases. . Curr. Opin. Struct. Biol. 3:(1):3138
    [Crossref] [Google Scholar]
  5. 5.
    Cramer P, Armache K-J, Baumli S, Benkert S, Brueckner F, et al. 2008.. Structure of eukaryotic RNA polymerases. . Annu. Rev. Biophys. 37::33752
    [Crossref] [Google Scholar]
  6. 6.
    Hanna R. 2000.. Metal ions in ribozyme folding and catalysis. . Curr. Opin. Chem. Biol. 4:(2):16670
    [Crossref] [Google Scholar]
  7. 7.
    Yang W. 2011.. Nucleases: diversity of structure, function and mechanism. . Q. Rev. Biophys. 44:(1):193
    [Crossref] [Google Scholar]
  8. 8.
    Horton NC, Newberry KJ, Perona JJ. 1998.. Metal ion-mediated substrate-assisted catalysis in type II restriction endonucleases. . PNAS 95:(23):1348994
    [Crossref] [Google Scholar]
  9. 9.
    DeRose VJ. 2003.. Metal ion binding to catalytic RNA molecules. . Curr. Opin. Struct. Biol. 13:(3):31724
    [Crossref] [Google Scholar]
  10. 10.
    Wilson TJ, Lilley DMJ. 2009.. The evolution of ribozyme chemistry. . Science 323:(5920):143638
    [Crossref] [Google Scholar]
  11. 11.
    Sigel RKO, Pyle AM. 2007.. Alternative roles for metal ions in enzyme catalysis and the implications for ribozyme chemistry. . Chem. Rev. 107:(1):97113
    [Crossref] [Google Scholar]
  12. 12.
    Pyle AM. 2016.. Group II intron self-splicing. . Annu. Rev. Biophys. 45::183205
    [Crossref] [Google Scholar]
  13. 13.
    Wilkinson ME, Charenton C, Nagai K. 2020.. RNA splicing by the spliceosome. . Annu. Rev. Biochem. 89::35988
    [Crossref] [Google Scholar]
  14. 14.
    Sakaguchi R, Lahoud G, Christian T, Gamper H, Hou Y-M. 2014.. A divalent metal ion-dependent N1 -methyl transfer to G37-tRNA. . Chem. Biol. 21:(10):135160
    [Crossref] [Google Scholar]
  15. 15.
    Levin JD, Shapiro R, Demple B. 1991.. Metalloenzymes in DNA repair. Escherichia coli endonuclease IV and Saccharomyces cerevisiae Apn1. . J. Biol. Chem. 266:(34):2289398
    [Crossref] [Google Scholar]
  16. 16.
    Berta D, Buigues PJ, Badaoui M, Rosta E. 2020.. Cations in motion: QM/MM studies of the dynamic and electrostatic roles of H+ and Mg2+ ions in enzyme reactions. . Curr. Opin. Struct. Biol. 61::198206
    [Crossref] [Google Scholar]
  17. 17.
    Palermo G, Cavalli A, Klein ML, Alfonso-Prieto M, Dal Peraro M, De Vivo M. 2015.. Catalytic metal ions and enzymatic processing of DNA and RNA. . Acc. Chem. Res. 48:(2):22028
    [Crossref] [Google Scholar]
  18. 18.
    Valdez CE, Smith QA, Nechay MR, Alexandrova AN. 2014.. Mysteries of metals in metalloenzymes. . Acc. Chem. Res. 47:(10):311017
    [Crossref] [Google Scholar]
  19. 19.
    Blomberg MRA, Borowski T, Himo F, Liao R-Z, Siegbahn PEM. 2014.. Quantum chemical studies of mechanisms for metalloenzymes. . Chem. Rev. 114:(7):360158
    [Crossref] [Google Scholar]
  20. 20.
    Rosta E, Nowotny M, Yang W, Hummer G. 2011.. Catalytic mechanism of RNA backbone cleavage by ribonuclease H from quantum mechanics/molecular mechanics simulations. . J. Am. Chem. Soc. 133:(23):893441
    [Crossref] [Google Scholar]
  21. 21.
    Rosta E, Yang W, Hummer G. 2014.. Calcium inhibition of ribonuclease H1 two-metal ion catalysis. . J. Am. Chem. Soc. 136:(8):313744
    [Crossref] [Google Scholar]
  22. 22.
    De Vivo M, Dal Peraro M, Klein ML. 2008.. Phosphodiester cleavage in ribonuclease H occurs via an associative two-metal-aided catalytic mechanism. . J. Am. Chem. Soc. 130:(33):1095562
    [Crossref] [Google Scholar]
  23. 23.
    Manigrasso J, De Vivo M, Palermo G. 2021.. Controlled trafficking of multiple and diverse cations prompts nucleic acid hydrolysis. . ACS Catal. 11:(14):878697
    [Crossref] [Google Scholar]
  24. 24.
    Palermo G, Stenta M, Cavalli A, Dal Peraro M, De Vivo M. 2013.. Molecular simulations highlight the role of metals in catalysis and inhibition of type II topoisomerase. . J. Chem. Theory Comput. 9:(2):85762
    [Crossref] [Google Scholar]
  25. 25.
    Ivanov I, Tainer JA, McCammon JA. 2007.. Unraveling the three-metal-ion catalytic mechanism of the DNA repair enzyme endonuclease IV. . PNAS 104:(5):146570
    [Crossref] [Google Scholar]
  26. 26.
    Genna V, Donati E, De Vivo M. 2018.. The catalytic mechanism of DNA and RNA polymerases. . ACS Catal. 8:(12):1110318
    [Crossref] [Google Scholar]
  27. 27.
    Roston D, Demapan D, Cui Q. 2019.. Extensive free-energy simulations identify water as the base in nucleotide addition by DNA polymerase. . PNAS 116:(50):2504856
    [Crossref] [Google Scholar]
  28. 28.
    Casalino L, Palermo G, Rothlisberger U, Magistrato A. 2016.. Who activates the nucleophile in ribozyme catalysis? An answer from the splicing mechanism of group II introns. . J. Am. Chem. Soc. 138:(33):1037477
    [Crossref] [Google Scholar]
  29. 29.
    Casalino L, Palermo G, Spinello A, Rothlisberger U, Magistrato A. 2018.. All-atom simulations disentangle the functional dynamics underlying gene maturation in the intron lariat spliceosome. . PNAS 115:(26):658489
    [Crossref] [Google Scholar]
  30. 30.
    Borišek J, Magistrato A. 2020.. All-atom simulations decrypt the molecular terms of RNA catalysis in the exon-ligation step of the spliceosome. . ACS Catal. 10:(9):532834
    [Crossref] [Google Scholar]
  31. 31.
    Chen JS, Doudna JA. 2017.. The chemistry of Cas9 and its CRISPR colleagues. . Nat. Rev. Chem. 1::0078
    [Crossref] [Google Scholar]
  32. 32.
    Saha A, Arantes PR, Palermo G. 2022.. Dynamics and mechanisms of CRISPR-Cas9 through the lens of computational methods. . Curr. Opin. Struct. Biol. 75::102400
    [Crossref] [Google Scholar]
  33. 33.
    Steitz TA, Steitz JA. 1993.. A general two-metal-ion mechanism for catalytic RNA. . PNAS 90:(14):6498502
    [Crossref] [Google Scholar]
  34. 34.
    Nagy GN, Suardíaz R, Lopata A, Ozohanics O, Vékey K, et al. 2016.. Structural characterization of arginine fingers: identification of an arginine finger for the pyrophosphatase dUTPases. . J. Am. Chem. Soc. 138:(45):1503545
    [Crossref] [Google Scholar]
  35. 35.
    Palermo G. 2019.. Structure and dynamics of the CRISPR-Cas9 catalytic complex. . J. Chem. Inf. Model. 59:(5):2394406
    [Crossref] [Google Scholar]
  36. 36.
    Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E. 2012.. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. . Science 337::81621
    [Crossref] [Google Scholar]
  37. 37.
    Doudna JA. 2020.. The promise and challenge of therapeutic genome editing. . Nature 578:(7794):22936
    [Crossref] [Google Scholar]
  38. 38.
    Jiang F, Taylor DW, Chen JS, Kornfeld JE, Zhou K, et al. 2016.. Structures of a CRISPR-Cas9 R-loop complex primed for DNA cleavage. . Science 351:(6275):86771
    [Crossref] [Google Scholar]
  39. 39.
    Li P, Merz KM. 2014.. Taking into account the ion-induced dipole interaction in the nonbonded model of ions. . J. Chem. Theory Comput. 10:(1):28997
    [Crossref] [Google Scholar]
  40. 40.
    Wang J, Arantes PR, Bhattarai A, Hsu RV, Pawnikar S, et al. 2021.. Gaussian accelerated molecular dynamics: principles and applications. . WIREs Comp. Mol. Sci. 11:(5):e1521
    [Crossref] [Google Scholar]
  41. 41.
    Bravo JPK, Liu M-S, Hibshman GN, Dangerfield TL, Jung K, et al. 2022.. Structural basis for mismatch surveillance by CRISPR-Cas9. . Nature 603:(7900):34347
    [Crossref] [Google Scholar]
  42. 42.
    Lee C, Yang W, Parr RG. 1988.. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. . Phys. Rev. B 37:(2):78589
    [Crossref] [Google Scholar]
  43. 43.
    Becke AD. 1988.. Density-functional exchange-energy approximation with correct asymptotic behavior. . Phys. Rev. A 38:(6):3098100
    [Crossref] [Google Scholar]
  44. 44.
    Casalino L, Nierzwicki Ł, Jinek M, Palermo G. 2020.. Catalytic mechanism of non-target DNA cleavage in CRISPR-Cas9 revealed by ab initio molecular dynamics. . ACS Catal. 10:(22):13596605
    [Crossref] [Google Scholar]
  45. 45.
    Nierzwicki Ł, Ahsan M, Palermo G. 2023.. The electronic structure of genome editors from the first principles. . Electron. Struct. 5:(1):014003
    [Crossref] [Google Scholar]
  46. 46.
    Warshel A, Levitt M. 1976.. Theoretical studies of enzymic reactions: dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme. . J. Mol. Biol. 103:(2):22749
    [Crossref] [Google Scholar]
  47. 47.
    Brunk E, Tavernelli I, Vanni S, Penfold TJ, Neri M, et al. 2011.. Pushing the frontiers of first-principles based computer simulations of chemical and biological systems. . Chimia 65:(9):66771
    [Crossref] [Google Scholar]
  48. 48.
    Marx D, Hutter J. 2009.. Ab Initio Molecular Dynamics: Basic Theory and Advanced Methods. New York:: Cambridge Univ. Press
    [Google Scholar]
  49. 49.
    Li P, Merz KM. 2017.. Metal ion modeling using classical mechanics. . Chem. Rev. 117:(3):1564686
    [Crossref] [Google Scholar]
  50. 50.
    Bock CW, Kaufman Katz A, Markham GD, Glusker JP. 1999.. Manganese as a replacement for magnesium and zinc: functional comparison of the divalent ions. . J. Am. Chem. Soc. 121:(32):736072
    [Crossref] [Google Scholar]
  51. 51.
    Wedekind JE, Dutta D, Belashov IA, Jenkins JL. 2017.. Metalloriboswitches: RNA-based inorganic ion sensors that regulate genes. . J. Biol. Chem. 292:(23):944150
    [Crossref] [Google Scholar]
  52. 52.
    Leonarski F, D'Ascenzo L, Auffinger P. 2017.. Mg2+ ions: Do they bind to nucleobase nitrogens?. Nucleic Acids Res. 45:(2):9871004
    [Crossref] [Google Scholar]
  53. 53.
    Nishimasu H, Ran FA, Hsu PD, Konermann S, Shehata SI, et al. 2014.. Crystal structure of Cas9 in complex with guide RNA and target DNA. . Cell 156:(5):93549
    [Crossref] [Google Scholar]
  54. 54.
    Cisneros GA, Perera L, Schaaper RM, Pedersen LC, London RE, et al. 2009.. Reaction mechanism of the ε subunit of E. coli DNA polymerase III: insights into active site metal coordination and catalytically significant residues. . J. Am. Chem. Soc. 131:(4):155056
    [Crossref] [Google Scholar]
  55. 55.
    Dürr SL, Bohuszewicz O, Berta D, Suardiaz R, Jambrina PG, et al. 2021.. The role of conserved residues in the DEDDh motif: the proton-transfer mechanism of HIV-1 RNase H. . ACS Catal. 11:(13):791527
    [Crossref] [Google Scholar]
  56. 56.
    Gong S, Yu HH, Johnson KA, Taylor DW. 2018.. DNA unwinding is the primary determinant of CRISPR-Cas9 activity. . Cell Rep. 22:(2):35971
    [Crossref] [Google Scholar]
  57. 57.
    Pacesa M, Loeff L, Querques I, Muckenfuss LM, Sawicka M, Jinek M. 2022.. R-loop formation and conformational activation mechanisms of Cas9. . Nature 609:(7925):19196
    [Crossref] [Google Scholar]
  58. 58.
    Galburt EA, Stoddard BL. 2002.. Catalytic mechanisms of restriction and homing endonucleases. . Biochemistry 41:(47):1385160
    [Crossref] [Google Scholar]
  59. 59.
    Nierzwicki L, East KW, Binz MB, Hsu RV, Ahsan M, et al. 2022.. Principles of target DNA cleavage and role of Mg2+ in the catalysis of CRISPR-Cas9. . Nat. Catal. 5:(10):91222
    [Crossref] [Google Scholar]
  60. 60.
    Palermo G, Miao Y, Walker RC, Jinek M, McCammon JA. 2016.. Striking plasticity of CRISPR-Cas9 and key role of non-target DNA, as revealed by molecular simulations. . ACS Cent. Sci. 2:(10):75663
    [Crossref] [Google Scholar]
  61. 61.
    East KW, Newton JC, Morzan UN, Narkhede YB, Acharya A, et al. 2020.. Allosteric motions of the CRISPR-Cas9 HNH nuclease probed by NMR and molecular dynamics. . J. Am. Chem. Soc. 142:(3):134858
    [Crossref] [Google Scholar]
  62. 62.
    Nierzwicki L, East KW, Morzan UN, Arantes PR, Batista VS, et al. 2021.. Enhanced specificity mutations perturb allosteric signaling in CRISPR-Cas9. . eLife 10::e73601
    [Crossref] [Google Scholar]
  63. 63.
    Skeens E, Sinha S, Mohd A, D'Ordine AM, Jogl G, et al. 2024.. High-fidelity, hyper-accurate, and evolved mutants rewire atomic level communication in CRISPR-Cas9. . Sci. Adv. 10:(10):eadl1045
    [Crossref] [Google Scholar]
  64. 64.
    Zuo Z, Liu J. 2017.. Structure and dynamics of Cas9 HNH domain catalytic state. . Sci. Rep. 7:(1):17271
    [Crossref] [Google Scholar]
  65. 65.
    Zhao LN, Mondal D, Warshel A. 2019.. Exploring alternative catalytic mechanisms of the Cas9 HNH domain. . Prot. Struct. Funct. Bioinf. 8:(2):26064
    [Crossref] [Google Scholar]
  66. 66.
    Yoon H, Zhao LN, Warshel A. 2019.. Exploring the catalytic mechanism of Cas9 using information inferred from endonuclease VII. . ACS Catal. 9:(2):132936
    [Crossref] [Google Scholar]
  67. 67.
    Huai G, Li G, Yao R, Zhang Y, Cao M, et al. 2017.. Structural insights into DNA cleavage activation of CRISPR-Cas9 system. . Nat. Commun. 8::1375
    [Crossref] [Google Scholar]
  68. 68.
    Anders C, Niewoehner O, Duerst A, Jinek M. 2014.. Structural basis of PAM-dependent target DNA recognition by the Cas9 endonuclease. . Nature 513:(7519):56973
    [Crossref] [Google Scholar]
  69. 69.
    Zhu X, Clarke R, Puppala AK, Chittori S, Merk A, et al. 2019.. Cryo-EM structures reveal coordinated domain motions that govern DNA cleavage by Cas9. . Nat. Struct. Mol. Biol. 26:(8):67985
    [Crossref] [Google Scholar]
  70. 70.
    Zuo Z, Zolekar A, Babu K, Lin VJ, Hayatshahi HS, et al. 2019.. Structural and functional insights into the bona fide catalytic state of Streptococcus pyogenes Cas9 HNH nuclease domain. . eLife 8::e46500
    [Crossref] [Google Scholar]
  71. 71.
    Swails JM, York DM, Roitberg AE. 2014.. Constant pH replica exchange molecular dynamics in explicit solvent using discrete protonation states: implementation, testing, and validation. . J. Chem. Theory Comput. 10:(3):134152
    [Crossref] [Google Scholar]
  72. 72.
    Laio A, VandeVondele J, Rothlisberger U. 2002.. D-RESP: dynamically generated electrostatic potential derived charges from quantum mechanics/molecular mechanics simulations. . J. Phys. Chem. B 106:(29):73007
    [Crossref] [Google Scholar]
  73. 73.
    Carter EA, Ciccotti G, Hynes JT, Kapral R. 1989.. Constrained reaction coordinate dynamics for the simulation of rare events. . Chem. Phys. Lett. 156:(5):47277
    [Crossref] [Google Scholar]
  74. 74.
    Ciccotti G, Ferrario M, Hynes JT, Kapral R. 1989.. Constrained molecular dynamics and the mean potential for an ion pair in a polar solvent. . Chem. Phys. 129:(2):24151
    [Crossref] [Google Scholar]
  75. 75.
    Barducci A, Bonomi M, Parrinello M. 2011.. Metadynamics. . WIREs Comput. Mol. Sci. 1:(5):82643
    [Crossref] [Google Scholar]
  76. 76.
    Van R, Pan X, Rostami S, Liu J, Agarwal PK, et al. 2025.. Exploring CRISPR-Cas9 HNH-domain-catalyzed DNA cleavage using accelerated quantum mechanical molecular mechanical free energy simulation. . Biochemistry 64::28999
    [Crossref] [Google Scholar]
  77. 77.
    Maghsoud Y, Jayasinghe-Arachchige VM, Kumari P, Cisneros GA, Liu J. 2023.. Leveraging QM/MM and molecular dynamics simulations to decipher the reaction mechanism of the Cas9 HNH domain to investigate off-target effects. . J. Chem. Inf. Model. 63::683450
    [Crossref] [Google Scholar]
  78. 78.
    Wang Y, Mallon J, Wang H, Singh D, Jo MH, et al. 2021.. Real-time observation of Cas9 postcatalytic domain motions. . PNAS 118:(2):e2010650118
    [Crossref] [Google Scholar]
  79. 79.
    Cech TR, Zaug AJ, Grabowski PJ. 1981.. In vitro splicing of the ribosomal RNA precursor of tetrahymena: involvement of a guanosine nucleotide in the excision of the intervening sequence. . Cell 27:(3):48796
    [Crossref] [Google Scholar]
  80. 80.
    Al-Hashimi HM, Walter NG. 2008.. RNA dynamics: it is about time. . Curr. Opin. Struct. Biol. 18:(3):32129
    [Crossref] [Google Scholar]
  81. 81.
    Šponer J, Bussi G, Krepl M, Banáš P, Bottaro S, et al. 2018.. RNA structural dynamics as captured by molecular simulations: a comprehensive overview. . Chem. Rev. 118:(8):4177338
    [Crossref] [Google Scholar]
  82. 82.
    Palermo G, Casalino L, Magistrato A, McCammon JA. 2019.. Understanding the mechanistic basis of non-coding RNA through molecular dynamics simulations. . J. Struct. Biol. 206:(3):26779
    [Crossref] [Google Scholar]
  83. 83.
    Wilson TJ, Liu Y, Lilley DMJ. 2016.. Ribozymes and the mechanisms that underlie RNA catalysis. . Front. Chem. Sci. Eng. 10:(2):17885
    [Crossref] [Google Scholar]
  84. 84.
    Panteva MT, Dissanayake T, Chen H, Radak BK, Kuechler ER, et al. 2015.. Multiscale methods for computational RNA enzymology. . Methods Enzymol. 553::33574
    [Crossref] [Google Scholar]
  85. 85.
    Bevilacqua PC, Harris ME, Piccirilli JA, Gaines C, Ganguly A, et al. 2019.. An ontology for facilitating discussion of catalytic strategies of RNA-cleaving enzymes. . ACS Chem. Biol. 14:(6):106876
    [Crossref] [Google Scholar]
  86. 86.
    Emilsson GM, Nakamura S, Roth A, Breaker RR. 2003.. Ribozyme speed limits. . RNA 9:(8):90718
    [Crossref] [Google Scholar]
  87. 87.
    Schwartz EA, Bravo JPK, Ahsan M, Macias LA, McCafferty CL, et al. 2024.. RNA targeting and cleavage by the type III-Dv CRISPR effector complex. . Nat. Commun. 15:(1):3324
    [Crossref] [Google Scholar]
  88. 88.
    Santiago-Frangos A, Hall LN, Nemudraia A, Nemudryi A, Krishna P, et al. 2021.. Intrinsic signal amplification by type III CRISPR-Cas systems provides a sequence-specific SARS-CoV-2 diagnostic. . Cell Rep. Med. 2:(6):100319
    [Crossref] [Google Scholar]
  89. 89.
    Grüschow S, Adamson CS, White MF. 2021.. Specificity and sensitivity of an RNA targeting type III CRISPR complex coupled with a NucC endonuclease effector. . Nucleic Acids Res. 49:(22):1312234
    [Crossref] [Google Scholar]
  90. 90.
    Suslov NB, DasGupta S, Huang H, Fuller JR, Lilley DMJ, et al. 2015.. Crystal structure of the Varkud satellite ribozyme. . Nat. Chem. Biol. 11:(11):84046
    [Crossref] [Google Scholar]
  91. 91.
    Lee T-S, López CS, Giambaşu GM, Martick M, Scott WG, York DM. 2008.. Role of Mg2+ in hammerhead ribozyme catalysis from molecular simulation. . J. Am. Chem. Soc. 130:(10):305364
    [Crossref] [Google Scholar]
  92. 92.
    Martick M, Lee T-S, York DM, Scott WG. 2008.. Solvent structure and hammerhead ribozyme catalysis. . Chem. Biol. 15:(4):33242
    [Crossref] [Google Scholar]
  93. 93.
    Kostenbader K, York DM. 2019.. Molecular simulations of the pistol ribozyme: unifying the interpretation of experimental data and establishing functional links with the hammerhead ribozyme. . RNA 25:(11):143956
    [Crossref] [Google Scholar]
  94. 94.
    Ren A, Vušurović N, Gebetsberger J, Gao P, Juen M, et al. 2016.. Pistol ribozyme adopts a pseudoknot fold facilitating site-specific in-line cleavage. . Nat. Chem. Biol. 12:(9):7028
    [Crossref] [Google Scholar]
  95. 95.
    DasGupta S, Suslov NB, Piccirilli JA. 2017.. Structural basis for substrate helix remodeling and cleavage loop activation in the Varkud satellite ribozyme. . J. Am. Chem. Soc. 139:(28):959197
    [Crossref] [Google Scholar]
  96. 96.
    Ganguly A, Thaplyal P, Rosta E, Bevilacqua PC, Hammes-Schiffer S. 2014.. Quantum mechanical/molecular mechanical free energy simulations of the self-cleavage reaction in the hepatitis delta virus ribozyme. . J. Am. Chem. Soc. 136:(4):148396
    [Crossref] [Google Scholar]
  97. 97.
    Gaines CS, Piccirilli JA, York DM. 2020.. The L-platform/L-scaffold framework: a blueprint for RNA-cleaving nucleic acid enzyme design. . RNA 26:(2):11125
    [Crossref] [Google Scholar]
  98. 98.
    Nierzwicki Ł, Palermo G. 2021.. Molecular dynamics to predict cryo-EM: capturing transitions and short-lived conformational states of biomolecules. . Front. Mol. Biosci. 8::641208
    [Crossref] [Google Scholar]
  99. 99.
    Tian C, Kasavajhala K, Belfon KAA, Raguette L, Huang H, et al. 2020.. ff19SB: amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. . J. Chem. Theory Comput. 16:(1):52852
    [Crossref] [Google Scholar]
  100. 100.
    Galindo-Murillo R, Robertson JC, Zgarbová M, Šponer J, Otyepka M, et al. 2016.. Assessing the current state of amber force field modifications for DNA. . J. Chem. Theory Comput. 12:(8):411427
    [Crossref] [Google Scholar]
  101. 101.
    Zgarbova M, Otyepka M, Sponer J, Mladek A, Banas P, et al. 2011.. Refinement of the Cornell et al. nucleic acids force field based on reference quantum chemical calculations of glycosidic torsion profiles. . J. Chem. Theory Comput. 7:(9):2886902
    [Crossref] [Google Scholar]
  102. 102.
    Izadi S, Anandakrishnan R, Onufriev AV. 2014.. Building water models: a different approach. . J. Phys. Chem. Lett. 5:(21):386371
    [Crossref] [Google Scholar]
  103. 103.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML. 1983.. Comparison of simple potential functions for simulating liquid water. . J. Chem. Phys. 79:(2):92635
    [Crossref] [Google Scholar]
  104. 104.
    Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C. 2015.. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. . J. Chem. Theory Comput. 11:(8):3696713
    [Crossref] [Google Scholar]
  105. 105.
    Ekesan Ş, York DM. 2019.. Dynamical ensemble of the active state and transition state mimic for the RNA-cleaving 8–17 DNAzyme in solution. . Nucleic Acids Res. 47:(19):1028295
    [Crossref] [Google Scholar]
  106. 106.
    Joung IS, Cheatham TE. 2008.. Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations. . J. Phys. Chem. B 112:(30):902041
    [Crossref] [Google Scholar]
  107. 107.
    Panteva MT, Giambaşu GM, York DM. 2015.. Comparison of structural, thermodynamic, kinetic and mass transport properties of Mg2+ ion models commonly used in biomolecular simulations. . J. Comput. Chem. 36:(13):97082
    [Crossref] [Google Scholar]
  108. 108.
    Panteva MT, Giambaşu GM, York DM. 2015.. Force field for Mg2+, Mn2+, Zn2+, and Cd2+ ions that have balanced interactions with nucleic acids. . J. Phys. Chem. B 119:(50):1546070
    [Crossref] [Google Scholar]
  109. 109.
    Grotz KK, Schwierz N. 2022.. Optimized magnesium force field parameters for biomolecular simulations with accurate solvation, ion-binding, and water-exchange properties in SPC/E, TIP3P-fb, TIP4P/2005, TIP4P-Ew, and TIP4P-D. . J. Chem. Theory Comput. 18:(1):52637
    [Crossref] [Google Scholar]
  110. 110.
    Li P, Merz KM. 2016.. MCPB.py: a python based metal center parameter builder. . J. Chem. Inf. Model. 56:(4):599604
    [Crossref] [Google Scholar]
  111. 111.
    Li P, Merz KM. 2021.. Parameterization of a dioxygen binding metal site using the MCPB.py program. . Methods Mol. Biol. 2199::25775
    [Crossref] [Google Scholar]
  112. 112.
    Becke AD. 1993.. A new mixing of Hartree–Fock and local density-functional theories. . J. Chem. Phys. 98:(2):137277
    [Crossref] [Google Scholar]
  113. 113.
    Frisch MJ, Pople JA, Binkley JS. 1984.. Self-consistent molecular orbital methods 25. Supplementary functions for Gaussian basis sets. . J. Chem. Phys. 80:(7):326569
    [Crossref] [Google Scholar]
  114. 114.
    Jing Z, Liu C, Cheng SY, Qi R, Walker BD, et al. 2019.. Polarizable force fields for biomolecular simulations: recent advances and applications. . Annu. Rev. Biophys. 48::37194
    [Crossref] [Google Scholar]
  115. 115.
    Lemkul JA, Huang J, Roux B, MacKerell AD. 2016.. An empirical polarizable force field based on the classical Drude oscillator model: development history and recent applications. . Chem. Rev. 116:(9):49835013
    [Crossref] [Google Scholar]
  116. 116.
    Olsen JMH, Bolnykh V, Meloni S, Ippoliti E, Bircher MP, et al. 2019.. MiMiC: a novel framework for multiscale modeling in computational chemistry. . J. Chem. Theory Comput. 15:(6):381023
    [Crossref] [Google Scholar]
  117. 117.
    Liberatore E, Meli R, Rothlisberger U. 2018.. A versatile multiple time step scheme for efficient ab initio molecular dynamics simulations. . J. Chem. Theory Comput. 14:(6):283442
    [Crossref] [Google Scholar]
  118. 118.
    Dürr SL, Levy A, Rothlisberger U. 2023.. Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins. . Nat. Commun. 14:(1):2713
    [Crossref] [Google Scholar]
  119. 119.
    Feehan R, Franklin MW, Slusky JSG. 2021.. Machine learning differentiates enzymatic and non-enzymatic metals in proteins. . Nat. Commun. 12:(1):3712
    [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-physchem-082423-030241
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